Data Exploration

Data exploration is the initial step in data analysis, where users explore a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest. This process isn’t meant to reveal every bit of information a dataset holds, but rather to help create a broad picture of important trends and major points to study in greater detail.

Data exploration can use a combination of manual methods and automated tools such as data visualizations, charts, and initial reports.

What are the steps of the process of data exploration?

You perform feature engineering once you have completed the first 5 steps in data exploration – Variable Identification, Univariate, Bivariate Analysis, Missing Values Imputation and Outliers Treatment.

What are the two main categories of techniques for exploring data?

There are two main categories of techniques to explore your data, one based on summary statistics and the other based on visualization methods. Summary statistics provide important information that summarizes a set of data values.

What do you do in data exploration?

Data exploration is the initial step in data analysis, where users explore a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest.

What is the difference between data exploration and predictive analytics?

Although seemingly similar, these are two very different concepts that serve very different purposes. See the full article for more details.